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Volumn 2015-January, Issue January, 2015, Pages 637-644

Out-of-sample error estimation: The blessing of high dimensionality

Author keywords

Classification; Error Estimation; High Dimensional Problems; Performance Estimation; Small Sample; Supervised Learning

Indexed keywords

DATA MINING; ERROR ANALYSIS; ERRORS; GENE EXPRESSION; SUPERVISED LEARNING;

EID: 84936875546     PISSN: 23759232     EISSN: 23759259     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2014.41     Document Type: Conference Paper
Times cited : (2)

References (45)
  • 1
    • 0346061723 scopus 로고    scopus 로고
    • High-dimensional data analysis: The curses and blessings of dimensionality
    • D. L. Donoho, "High-dimensional data analysis: The curses and blessings of dimensionality, " AMS Math Challenges Lecture, pp. 1-32, 2000.
    • (2000) AMS Math Challenges Lecture , pp. 1-32
    • Donoho, D.L.1
  • 2
    • 84984934346 scopus 로고    scopus 로고
    • Gene expression informatics-it's all in your mine
    • D. E. Bassett, M. B. Eisen, and M. S. Boguski, "Gene expression informatics-it's all in your mine, " Nature genetics, vol. 21, pp. 51-55, 1999.
    • (1999) Nature Genetics , vol.21 , pp. 51-55
    • Bassett, D.E.1    Eisen, M.B.2    Boguski, M.S.3
  • 3
    • 84889691471 scopus 로고    scopus 로고
    • Introduction to biomedical imaging
    • A. Webb and G. C. Kagadis, "Introduction to biomedical imaging, " Medical Physics, vol. 30, no. 8, pp. 2267-2267, 2003.
    • (2003) Medical Physics , vol.30 , Issue.8 , pp. 2267-2267
    • Webb, A.1    Kagadis, G.C.2
  • 4
    • 49449118343 scopus 로고    scopus 로고
    • Robust machine learning applied to astronomical data sets probabilistic photometric redshifts for galaxies and quasars in the sdss and galex
    • N. M. Ball, R. J. Brunner, A. D. Myers, N. E. Strand, S. L. Alberts, and D. Tcheng, "Robust machine learning applied to astronomical data sets probabilistic photometric redshifts for galaxies and quasars in the sdss and galex, " The Astrophysical Journal, vol. 683, no. 1, pp. 12-21, 2008.
    • (2008) The Astrophysical Journal , vol.683 , Issue.1 , pp. 12-21
    • Ball, N.M.1    Brunner, R.J.2    Myers, A.D.3    Strand, N.E.4    Alberts, S.L.5    Tcheng, D.6
  • 5
    • 0037076322 scopus 로고    scopus 로고
    • Selection bias in gene extraction on the basis of microarray gene-expression data
    • C. Ambroise and G. J. McLachlan, "Selection bias in gene extraction on the basis of microarray gene-expression data, " Proceedings of the national academy of sciences, vol. 99, no. 10, pp. 6562-6566, 2002.
    • (2002) Proceedings of the National Academy of Sciences , vol.99 , Issue.10 , pp. 6562-6566
    • Ambroise, C.1    McLachlan, G.J.2
  • 7
    • 84902167747 scopus 로고    scopus 로고
    • On criticality in highdimensional data
    • S. Saremi and T. J. Sejnowski, "On criticality in highdimensional data, " Neural computation, vol. 26, no. 7, pp. 1329-1339, 2014.
    • (2014) Neural Computation , vol.26 , Issue.7 , pp. 1329-1339
    • Saremi, S.1    Sejnowski, T.J.2
  • 8
    • 1342330535 scopus 로고    scopus 로고
    • Is cross-validation valid for small-sample microarray classification"
    • U. M. Braga-Neto and E. R. Dougherty, "Is cross-validation valid for small-sample microarray classification" Bioinformatics, vol. 20, no. 3, pp. 374-380, 2004.
    • (2004) Bioinformatics , vol.20 , Issue.3 , pp. 374-380
    • Braga-Neto, U.M.1    Dougherty, E.R.2
  • 9
    • 84875879529 scopus 로고    scopus 로고
    • In-sample and out-of-sample model selection and error estimation for support vector machines
    • D. Anguita, A. Ghio, L. Oneto, and S. Ridella, "In-sample and out-of-sample model selection and error estimation for support vector machines, " IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 9, pp. 1390-1406, 2012.
    • (2012) IEEE Transactions on Neural Networks and Learning Systems , vol.23 , Issue.9 , pp. 1390-1406
    • Anguita, D.1    Ghio, A.2    Oneto, L.3    Ridella, S.4
  • 11
    • 24944572401 scopus 로고    scopus 로고
    • Maps for the visualization of high-dimensional data spaces
    • A. Ultsch, "Maps for the visualization of high-dimensional data spaces, " in Workshop on Self-Organizing Maps, 2003.
    • (2003) Workshop on Self-Organizing Maps
    • Ultsch, A.1
  • 14
    • 81955161252 scopus 로고    scopus 로고
    • Non-parametric detection of meaningless distances in high dimensional data
    • A. Kabán, "Non-parametric detection of meaningless distances in high dimensional data, " Statistics and Computing, vol. 22, no. 2, pp. 375-385, 2012.
    • (2012) Statistics and Computing , vol.22 , Issue.2 , pp. 375-385
    • Kabán, A.1
  • 17
    • 33746036704 scopus 로고    scopus 로고
    • Towards a theoretical foundation for laplacian-based manifold methods
    • M. Belkin and P. Niyogi, "Towards a theoretical foundation for laplacian-based manifold methods, " in Learning theory, 2005.
    • (2005) Learning Theory
    • Belkin, M.1    Niyogi, P.2
  • 19
  • 21
    • 84936853791 scopus 로고    scopus 로고
    • The more, the merrier: The blessing of dimensionality for learning large Gaussian mixtures
    • J. Anderson, M. Belkin, N. Goyal, R. L., and J. Voss, "The more, the merrier: the blessing of dimensionality for learning large Gaussian mixtures, " Journal of Machine Learning Research, vol. 35, pp. 1-30, 2014.
    • (2014) Journal of Machine Learning Research , vol.35 , pp. 1-30
    • Anderson, J.1    Belkin, M.2    Goyal R L, N.3    Voss, J.4
  • 22
    • 0000028873 scopus 로고    scopus 로고
    • A reality check for data snooping
    • H. White, "A reality check for data snooping, " Econometrica, vol. 68, no. 5, pp. 1097-1126, 2000.
    • (2000) Econometrica , vol.68 , Issue.5 , pp. 1097-1126
    • White, H.1
  • 23
    • 21844472595 scopus 로고    scopus 로고
    • Data snooping, dredging and fishing: The dark side of data mining a sigkdd99 panel report
    • D. Jensen, "Data snooping, dredging and fishing: The dark side of data mining a sigkdd99 panel report, " ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, pp. 52-54, 2000.
    • (2000) ACM SIGKDD Explorations Newsletter , vol.1 , Issue.2 , pp. 52-54
    • Jensen, D.1
  • 24
    • 0036643049 scopus 로고    scopus 로고
    • Model selection and error estimation
    • P. L. Bartlett, S. Boucheron, and G. Lugosi, "Model selection and error estimation, " Machine Learning, vol. 48, no. 1-3, pp. 85-113, 2002.
    • (2002) Machine Learning , vol.48 , Issue.1-3 , pp. 85-113
    • Bartlett, P.L.1    Boucheron, S.2    Lugosi, G.3
  • 25
    • 79952972776 scopus 로고    scopus 로고
    • Data warehouses and data mining tools for the legal profession: Using information technology to raise the standard of practice
    • L. Roberge, S. Long, and D. Burnham, "Data warehouses and data mining tools for the legal profession: using information technology to raise the standard of practice, " Syracuse Law Review, vol. 52, pp. 1281-1292, 2002.
    • (2002) Syracuse Law Review , vol.52 , pp. 1281-1292
    • Roberge, L.1    Long, S.2    Burnham, D.3
  • 27
    • 0038453192 scopus 로고    scopus 로고
    • Rademacher and Gaussian complexities: Risk bounds and structural results
    • P. L. Bartlett and S. Mendelson, "Rademacher and Gaussian complexities: Risk bounds and structural results, " The Journal of Machine Learning Research, vol. 3, pp. 463-482, 2003.
    • (2003) The Journal of Machine Learning Research , vol.3 , pp. 463-482
    • Bartlett, P.L.1    Mendelson, S.2
  • 30
    • 21844462365 scopus 로고    scopus 로고
    • Tutorial on practical prediction theory for classification
    • J. Langford, "Tutorial on practical prediction theory for classification, " Journal of machine learning research, vol. 6, no. 1, p. 273, 2006.
    • (2006) Journal of Machine Learning Research , vol.6 , Issue.1 , pp. 273
    • Langford, J.1
  • 31
    • 0032594959 scopus 로고    scopus 로고
    • An overview of statistical learning theory
    • V. N. Vapnik, "An overview of statistical learning theory, " IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988-999, 1999.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , Issue.5 , pp. 988-999
    • Vapnik, V.N.1
  • 33
    • 0001072895 scopus 로고
    • The use of confidence or fiducial limits illustrated in the case of the binomial
    • C. J. Clopper and E. S. Pearson, "The use of confidence or fiducial limits illustrated in the case of the binomial, " Biometrika, vol. 26, no. 4, pp. 404-413, 1934.
    • (1934) Biometrika , vol.26 , Issue.4 , pp. 404-413
    • Clopper, C.J.1    Pearson, E.S.2
  • 36
    • 77956649096 scopus 로고    scopus 로고
    • A survey of cross-validation procedures for model selection
    • S. Arlot and A. Celisse, "A survey of cross-validation procedures for model selection, " Statistics Surveys, vol. 4, pp. 40-79, 2010.
    • (2010) Statistics Surveys , vol.4 , pp. 40-79
    • Arlot, S.1    Celisse, A.2
  • 39
    • 22544475586 scopus 로고    scopus 로고
    • GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data
    • A. Statnikov, I. Tsamardinos, and Y. Dosbayev, "GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data, " International Journal of Medical Informatics, vol. 74, no. 7-8, pp. 491-503, 2005.
    • (2005) International Journal of Medical Informatics , vol.74 , Issue.7-8 , pp. 491-503
    • Statnikov, A.1    Tsamardinos, I.2    Dosbayev, Y.3
  • 45
    • 0032139235 scopus 로고    scopus 로고
    • The random subspace method for constructing decision forests
    • T. K. Ho, "The random subspace method for constructing decision forests, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998.
    • (1998) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.20 , Issue.8 , pp. 832-844
    • Ho, T.K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.